This document forms part of the data and code deposited at:
https://github.com/acp29/Elmasri_GRIN2B
Load package requirements
if (!require(package="tidyverse")) utils::install.packages("tidyverse")
library(tidyverse)
if (!require(package="lme4")) utils::install.packages("lme4")
library(lme4)
if (!require(package="HLMdiag")) utils::install.packages("HLMdiag")
library(HLMdiag)
if (!require(package="parameters")) utils::install.packages("parameters")
## Warning: package 'parameters' was built under R version 4.1.2
library(parameters)
if (!require(package="car")) utils::install.packages("car")
library(car)
if (!require(package="performance")) utils::install.packages("performance")
library(performance)
if (!require(package="BayesFactor")) utils::install.packages("BayesFactor")
library(BayesFactor)
if (!require(package="bayestestR")) utils::install.packages("bayestestR")
library(bayestestR)
if (!require(package="stats")) utils::install.packages("stats")
library(stats)
if (!require(package="pCalibrate")) utils::install.packages("pCalibrate")
library(pCalibrate)
if (!require(package="afex")) utils::install.packages("afex")
library(afex)
if (!require(package="emmeans")) utils::install.packages("emmeans")
library(emmeans)
if (!require(package="multcomp")) utils::install.packages("multcomp")
library(multcomp)
if (!require(package="knitr")) utils::install.packages("knitr")
library(knitr)
if (!require(package="kableExtra")) utils::install.packages("kableExtra")
library(kableExtra)
if (!require(package="ggplot2")) utils::install.packages("ggplot2")
library(ggplot2)
if (!require(package="qqplotr")) utils::install.packages("qqplotr")
library(qqplotr)
if (!require(package="gridExtra")) utils::install.packages("gridExtra")
library(gridExtra)
if (!require(package="ggforce")) utils::install.packages("ggforce")
library(ggforce)
if (!require(package="devEMF")) utils::install.packages("devEMF")
library(devEMF)
if (!require(package="effectsize")) utils::install.packages("effectsize")
library(effectsize)
Read text in from file
Data <- read.delim("../data/n2b_mutant.dat", header = TRUE)
# Explicit nesting (required for anovaBF)
Data %>%
mutate(mutation = as.factor(mutation)) %>%
mutate(animal = paste0(as.numeric(mutation),animal)) %>%
mutate(slice = paste0(animal,slice)) %>%
mutate(pair = paste0(slice,pair)) %>%
mutate(pair = factor(pair)) -> Data
Factor encoding
Data$mutation <- as.factor(Data$mutation)
Data$transfection <- as.factor(Data$transfection)
Data$animal <- as.factor(Data$animal)
Data$slice <- as.factor(Data$slice)
Data$pair <- as.factor(Data$pair)
Set mutation WT and transfection - as reference levels
Data$mutation <- factor(Data$mutation, levels=c("WT","R540H","R696H","C456Y","C461F"))
Data$transfection <- factor(Data$transfection, levels=c("-","+"))
lmer settings
settings <- lmerControl(check.conv.singular = .makeCC(action = "ignore", tol = 1e-4), boundary.tol=0)
Fit a mixed linear model
# Initialize
variates <- c("peaknmda","decaynmda","dt50nmda","chargenmda","peakampa","decayampa","dt50ampa","chargeampa")
l <- length(variates)
for (i in 1:l) {
variates[i] -> resp
cat('\n\n\n# Analysis of',resp,'\n\n')
# Plot data
# colours selected from:
# > library(scales)
# > show_col(hue_pal()(9))
p1 <- Data %>%
mutate(mutation_jittered = jitter((as.numeric(mutation)+(as.numeric(transfection)-1)/2.5), 0.5),
grouping=interaction(pair, mutation)) %>%
mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) %>%
ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=transfection)) +
geom_blank() +
geom_line(aes(mutation_jittered), alpha=0.2, color="grey") +
geom_point(aes(mutation_jittered), alpha=0.4, shape=16) +
scale_color_manual(values=c("grey","#00BA38")) +
stat_summary(mapping = aes(x=mutation_transfection,y=!!sym(resp)), fun.data="median_hilow", fun.args = list(conf.int=0.5), geom="linerange", color="black", size=1.0,inherit.aes=FALSE) +
stat_summary(mapping = aes(x=mutation_transfection,y=!!sym(resp)), fun="median", geom="point", shape=21, fill="white", color="black", size=2.5, stroke=1, inherit.aes=FALSE) +
ylab(resp) +
ggtitle("a") +
theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.title = element_blank(),
legend.position = "top")
p2 <- Data %>%
pivot_wider(c(mutation,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>%
mutate(ratio = `+`/`-`) %>%
ggplot(aes(x=mutation, y=ratio, colour=mutation)) +
geom_sina(alpha=0.9, shape = 16) +
scale_color_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
stat_summary(fun.data="median_hilow", fun.args = list(conf.int=0.5), geom="linerange", color="black", size=1.0) +
stat_summary(fun="median", geom="point", shape=21, fill="white", color="black", size=2.5, stroke=1) +
ylab("ratio") +
ggtitle("b") +
theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.position = "none")
grid.arrange(p1, p2, nrow = 1, ncol = 2, top=sprintf("Summary plots of the data for: %s\n",resp))
# Fit the model with planned contrasts and perform hypothesis testing
# Setup planned, orthogonal contrasts
# ("WT","R540H","R696H","C456Y","C461F")
# According to features of the mutations or experiments in heterologous expression systems
WT_vs_Mutants <- c(-4,1,1,1,1)/5
LOF_vs_GOF <- c(0,2,2,-2,-2)/4
LOF <- c(0,0,0,-1,1)/2
GOF <- c(0,-1,1,0,0)/2
contr.orth <- cbind(WT_vs_Mutants, LOF_vs_GOF, LOF, GOF)
rownames(contr.orth) <- levels(Data$mutation)
# Check that contrasts are indeed orthogonal
contr.orth %>%
cor() %>%
knitr::kable(caption = sprintf("**All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
contr.orth %>%
colSums() %>%
as.data.frame() %>%
rename(.,'sum' = '.') %>%
knitr::kable(caption = sprintf("**Sum of each orthogonal contrast should be zero: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
# Fit model (with orthogonal contrasts)
contrasts(Data$mutation) <- contr.orth
attr(Data$mutation,"contrasts") %>%
as.data.frame() %>%
rownames_to_column(var = "mutation") %>%
knitr::kable(caption = sprintf("**Matrix of contrasts on mutation: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
contrasts(Data$transfection) <- rbind(-1,1)/2
attr(Data$transfection,"contrasts") %>%
as.data.frame() %>%
rename(contrast = "V1") %>%
rownames_to_column(var = "transfection") %>%
mutate_at("transfection", str_replace_all, pattern = "\\+", replacement = "\\\\+") %>%
mutate_at("transfection", str_replace_all, pattern = "\\-", replacement = "\\\\-") %>%
knitr::kable(caption = sprintf("**Matrix of contrasts on transfection: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
formula <- sprintf("log(%s) ~ mutation * transfection + (1|animal/slice/pair)", resp)
model <- lme4::lmer(formula, data = Data, REML = TRUE, control = settings, na.action = "na.fail")
# Checking model assumptions
resid = residuals(model)
n = length(resid)
stdev = sqrt((n-1)/n) * sd(resid) # standard deviation with denominator n
std_resid = resid/stdev
p1 <- ggplot(Data, aes(x = fitted(model), y = std_resid)) +
geom_point() +
ggtitle("a") +
xlab("Fitted values") + ylab("Standardized Residuals") +
geom_hline(yintercept = 0) +
geom_quantile(formula=y~x, color="#619CFF", size=1) +
geom_smooth(method="loess", formula = y ~ x, color="#F8766D", size=1, se=FALSE)
p2 <- ggplot(Data, aes(x = std_resid)) +
geom_histogram(aes(y=..density..), binwidth = 0.9*n^(-1/5), fill="#619CFF", alpha=0.33) +
geom_density(kernel="gaussian", alpha=0, color="#619CFF", size=1) +
ggtitle("b") +
xlab("Standardized Residuals") + ylab("Density") +
geom_vline(xintercept = 0) +
geom_function(fun = dnorm, args = list(mean=0, sd=1), col = "#F8766D", size = 1)
p3 <- ggplot(Data, aes(sample = std_resid)) +
geom_qq_band(distribution = "norm", bandType = "ts", mapping = aes(fill = "TS"), fill="#619CFF", alpha = 0.33) +
stat_qq() +
stat_qq_line(color="#F8766D",size=1) +
ggtitle("c") +
xlab("Normal Quantiles") + ylab("Sample Quantiles")
infl <- hlm_influence(model, level="pair:(slice:animal)")
p4 <- infl %>%
mutate(influential = cooksd > 1.0) %>%
ggplot(aes(x=`pair:(slice:animal)`,y=cooksd, color=influential)) +
geom_segment(aes(x=`pair:(slice:animal)`, xend=`pair:(slice:animal)`, y=0, yend=cooksd)) +
geom_point() +
ylab("Cook's distance") +
scale_color_manual(values=c("#619CFF","#F8766D")) +
ggtitle("d") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "none",
panel.background = element_rect(color="#EBEBEB"),
panel.grid = element_blank(),
panel.grid.minor.y = element_line(color = "white", size=0.25),
panel.grid.major.y = element_line(color = "white", size=0.5),
axis.line = element_blank(),
axis.line.x = element_line(size = 0.5, colour = "black"))
grid.arrange(p1, p2, p3, p4, nrow=2, ncol=2, top=sprintf("Plots of standardized model residuals and Cook's distances: %s\n",resp))
# Calculate ANOVA table for the fitted model (Type III sum of squares)
car::Anova(model, type = 3, test.statistic = "F") %>% # Uses Kenward-Roger degrees of freedom
as.data.frame() %>%
rownames_to_column(var="Source") %>%
filter(Source != "(Intercept)") -> aov
# Calculate Bayes Factors for ANOVA and append them to the ANOVA data frame
# Inclusion Bayes Factor based on matched models (prior odds uniform-equal)
Data %>%
mutate(logresp = log(!!sym(resp))) %>%
as.data.frame() -> Data
set.seed(123456)
anovaBF(logresp ~ mutation * transfection + animal + slice + pair,
whichRandom = c("animal","slice","pair"),
whichModels = "withmain",
iterations = 20000,
data = Data) %>%
bayesfactor_inclusion(match_models = TRUE) %>%
as.data.frame() %>%
na.omit() %>% # removes the (nuisance) random factors
mutate(BF = exp(log_BF)) %>%
mutate_at("BF", formatC, format='g',digits = 3) %>%
dplyr::select(BF) %>%
unlist() -> aov$BF
# Calculate orthogonal contrasts and append them to the ANOVA summary table
# I go to the trouble of transforming the t-statistic (which is returned from the linear model)
# to an F statistic but they give identical p-values; I think this makes more sense and provides
# more consistency when splitting the source of variation into orthogonal contrasts and presenting
# them in an ANOVA table (eg. like with summary.aov or orthogonal contrasts in SAS)
model_parameters(model, df_method = "kenward", exponentiate = TRUE, effects = "fixed") %>%
filter(grepl(":",Parameter)) %>% # select interaction terms only
rename(Source = Parameter) %>% #
mutate(Df = 1) %>% # set numerator degrees of freedom
add_column(BF = "") %>% # add empty column for Bayes factors
rename(Df.res = df_error) %>% # set denominator degrees of freedom
mutate(F = abs(t)^2) %>% # calculate F statistic
mutate(`Pr(>F)` = pf(F,Df,Df.res,lower.tail=FALSE)) %>% # calculate p value
dplyr::select(c(Source,F,Df,Df.res,`Pr(>F)`,BF)) %>% # select columns of interest for table
rbind(aov,.) %>% # row bind with anova table
mutate(`Pr(>F)` = afex::round_ps_apa(`Pr(>F)`)) %>% # format p values as APA style
knitr::kable(caption = sprintf("**ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
add_indent(c(4:7)) %>% # add indentation to indicate source components
print()
# Calculate intraclass correlation coefficients (ICC) for the random effects
icc(model, by_group=TRUE, tolerance=0) %>%
as.data.frame() %>%
mutate(N = ngrps(model)) %>%
rbind(.,c("residual",1-sum(.$ICC),nobs(model))) %>%
mutate(ICC = as.numeric(ICC)) %>%
knitr::kable(caption = sprintf("**Intraclass correlation coefficients for random effects: %s**",resp), digits = 3) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
# Calculated estimated marginal means, By default, emmeans uses Kenward-Roger's method for estimating the degrees of freedom
emm <- emmeans(model, ~ mutation * transfection, data = Data, tran = 'log', type = 'response')
emm %>%
summary(calc = c(n = ".wgt.")) %>%
as.data.frame() %>%
mutate_at("transfection", str_replace_all, pattern = "\\+", replacement = "\\\\+") %>%
mutate_at("transfection", str_replace_all, pattern = "\\-", replacement = "\\\\-") %>%
relocate(df, .before = response) %>%
dplyr::select(-SE) %>%
knitr::kable(caption = sprintf("**Estimated marginal means with 95%% confidence intervals: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
# Calculate overall average for untransfected neurons
emmeans(model, ~ mutation * transfection, data = Data) %>%
as.data.frame() %>%
filter(transfection == "-") %>%
dplyr::select(emmean) %>%
colMeans() %>%
exp() %>%
sprintf("**Overall average of %s for untransfected neurons**: %.2f",resp,.) %>%
print()
# Calculate transfected/untransfected ratios
emm.transfection <- contrast(emm, method = "trt.vs.ctrl", interaction = FALSE, by = 'mutation', adjust = "none")
emm.transfection %>%
confint() %>%
as.data.frame() %>%
relocate(df, .before = ratio) %>%
dplyr::select(-SE) %>%
knitr::kable(caption = sprintf("**Estimated marginal means with 95%% confidence intervals for transfected/untransfected ratios: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
# 95% confidence intervals for interaction contrasts
emm.interaction <- contrast(emm, method = "trt.vs.ctrl", interaction = TRUE, adjust = "none")
emm.interaction %>%
confint() %>%
relocate(df, .before = ratio) %>%
dplyr::select(-SE) %>%
knitr::kable(caption = sprintf("**95%% confidence intervals for contrasts: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
# Standardized effect sizes (*r*) for interaction contrasts
# Methods used the same as this server: https://easystats4u.shinyapps.io/statistic2effectsize/
emm.interaction %>%
as.data.frame() %>%
mutate(n = df+nrow(.)+1) %>%
mutate(r = t_to_r(t.ratio, df)$r) %>%
mutate(z = atanh(r),
SE = 1/sqrt(n-3),
CI = sprintf("[%.2f, %.2f]",
LL = tanh(z - 1.96*SE),
UL = tanh(z + 1.96*SE))) %>%
dplyr::select(-c(ratio,SE,df,null,t.ratio,p.value,z)) %>%
knitr::kable(col.names = c("mutation",
"transfection",
"*n*",
"*r*",
"95% *CI*"),
caption = sprintf("**Standardized effect sizes (*r*) for contrasts: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
posthoc = FALSE
if (posthoc == TRUE) {
# p-values and maximum Bayes Factors for interaction contrasts
# Dunnett's step-down adjustment to control FWER on p-values (using multcomp package)
# Chapter 4.1.2 in Bretz, F., Hothorn, T. and Westfall, P. (2011) Multiple Comparisons Using R. Taylor and Frances Group, LLC.
emm.interaction %>%
as.glht() %>%
summary(test = adjusted(type = "free")) -> glht.out
emm.interaction %>%
as.data.frame() %>%
dplyr::select(-SE) %>%
mutate(p.adj = glht.out$test$pvalues) %>%
mutate(p.adj = sapply(p.adj,max,.Machine$double.eps)) %>%
mutate(maxBF = 1/pCalibrate(p.adj,"exploratory")) %>%
mutate_at("maxBF", formatC, format='g',digits = 3) %>%
mutate(p.value = afex::round_ps_apa(p.value)) %>%
mutate(p.adj = afex::round_ps_apa(p.adj)) %>%
knitr::kable(caption = sprintf("**Hypothesis testing on interaction parameters (Dunnett's step-down p-value adjustment): %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
}
# Replot data with 95% confidence intervals
emf(sprintf("../img/%s_%s.emf","n2b_mutants",resp), width=4.5, height=3.5)
emm %>%
as.data.frame() %>%
mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) -> emm_df
p1 <- Data %>%
mutate(mutation_jittered = jitter((as.numeric(mutation)+(as.numeric(transfection)-1)/2.5), 0.5),
grouping=interaction(pair, mutation)) %>%
mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) %>%
ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=transfection)) +
geom_blank() +
geom_line(aes(mutation_jittered), alpha=0.3, color="grey", size=0.75) +
geom_point(aes(mutation_jittered), alpha=0.6, shape = 16, size=1.25) +
scale_color_manual(values=c("grey","#00BA38")) +
scale_fill_manual(values=c("grey","#00BA38")) +
geom_crossbar(data = emm_df,
aes(x=mutation_transfection, y=response, ymin=`lower.CL`, ymax=`upper.CL`, fill=transfection),
color="black", alpha=0.5, size=0.5, fatten=1, width=0.3, inherit.aes=FALSE) +
ylab(resp) +
theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.title = element_blank(),
legend.position = c(0.5, 1.06),
legend.direction = "horizontal",
text = element_text(size=14))
emm.transfection %>%
confint() %>%
as.data.frame() -> emm.transfection_df
p2 <- Data %>%
pivot_wider(c(mutation,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>%
mutate(ratio = `+`/`-`) %>%
ggplot(aes(x=mutation, y=ratio, colour=mutation)) +
geom_sina(alpha=0.6, shape=16, size=1.25, maxwidth=0.5) +
geom_crossbar(data = emm.transfection_df,
aes(x=mutation, y=ratio, ymin=`lower.CL`, ymax=`upper.CL`, fill=mutation),
color="black", alpha=0.5, size=0.5, fatten=1, width=0.8, inherit.aes=FALSE) +
scale_colour_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
scale_fill_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
ylab("ratio") +
theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1), axis.line = element_line(colour="black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.position = "none",
text=element_text(size=14))
grid.arrange(p1, p2, layout_matrix=rbind(c(1,2)), top=sprintf("Summary plots of the data with 95%% confidence intervals: %s\n",resp))
dev.off() #turn off device and finalize file
}
Analysis of peaknmda
All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: peaknmda
|
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT_vs_Mutants
|
1
|
0
|
0
|
0
|
|
LOF_vs_GOF
|
0
|
1
|
0
|
0
|
|
LOF
|
0
|
0
|
1
|
0
|
|
GOF
|
0
|
0
|
0
|
1
|
Sum of each orthogonal contrast should be zero: peaknmda
|
|
sum
|
|
WT_vs_Mutants
|
0
|
|
LOF_vs_GOF
|
0
|
|
LOF
|
0
|
|
GOF
|
0
|
Matrix of contrasts on mutation: peaknmda
|
mutation
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT
|
-0.8
|
0.0
|
0.0
|
0.0
|
|
R540H
|
0.2
|
0.5
|
0.0
|
-0.5
|
|
R696H
|
0.2
|
0.5
|
0.0
|
0.5
|
|
C456Y
|
0.2
|
-0.5
|
-0.5
|
0.0
|
|
C461F
|
0.2
|
-0.5
|
0.5
|
0.0
|
Matrix of contrasts on transfection: peaknmda
|
transfection
|
contrast
|
|
-
|
-0.5
|
|
+
|
0.5
|
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: peaknmda
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
mutation
|
2.91
|
4
|
16.22
|
.055
|
2.01
|
|
transfection
|
49.75
|
1
|
90.00
|
<.001
|
5.87e+07
|
|
mutation:transfection
|
0.21
|
4
|
90.00
|
.933
|
0.052
|
|
mutationWT_vs_Mutants:transfection1
|
0.38
|
1
|
90.00
|
.540
|
|
|
mutationLOF_vs_GOF:transfection1
|
0.40
|
1
|
90.00
|
.530
|
|
|
mutationLOF:transfection1
|
0.07
|
1
|
90.00
|
.796
|
|
|
mutationGOF:transfection1
|
0.04
|
1
|
90.00
|
.845
|
|
Intraclass correlation coefficients for random effects: peaknmda
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.160
|
95
|
|
slice:animal
|
0.267
|
62
|
|
animal
|
0.363
|
22
|
|
residual
|
0.209
|
190
|
Estimated marginal means with 95% confidence intervals: peaknmda
|
mutation
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
16.59
|
162.39
|
19
|
86.29
|
305.61
|
|
R540H
|
-
|
17.91
|
93.71
|
26
|
54.83
|
160.16
|
|
R696H
|
-
|
18.08
|
43.03
|
17
|
22.63
|
81.83
|
|
C456Y
|
-
|
20.05
|
108.92
|
18
|
60.44
|
196.29
|
|
C461F
|
-
|
16.34
|
71.70
|
15
|
34.66
|
148.34
|
|
WT
|
+
|
16.59
|
117.93
|
19
|
62.66
|
221.95
|
|
R540H
|
+
|
17.91
|
66.16
|
26
|
38.71
|
113.07
|
|
R696H
|
+
|
18.08
|
29.42
|
17
|
15.47
|
55.94
|
|
C456Y
|
+
|
20.05
|
71.72
|
18
|
39.80
|
129.24
|
|
C461F
|
+
|
16.34
|
45.01
|
15
|
21.76
|
93.12
|
[1] “
Overall average of peaknmda for untransfected neurons: 87.45”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peaknmda
|
contrast
|
mutation
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
90
|
0.73
|
0.57
|
0.92
|
|
(+) / (-)
|
R540H
|
90
|
0.71
|
0.58
|
0.87
|
|
(+) / (-)
|
R696H
|
90
|
0.68
|
0.53
|
0.88
|
|
(+) / (-)
|
C456Y
|
90
|
0.66
|
0.51
|
0.84
|
|
(+) / (-)
|
C461F
|
90
|
0.63
|
0.48
|
0.82
|
95% confidence intervals for contrasts: peaknmda
|
mutation_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
R540H / WT
|
(+) / (-)
|
90
|
0.97
|
0.71
|
1.33
|
|
R696H / WT
|
(+) / (-)
|
90
|
0.94
|
0.66
|
1.33
|
|
C456Y / WT
|
(+) / (-)
|
90
|
0.91
|
0.64
|
1.28
|
|
C461F / WT
|
(+) / (-)
|
90
|
0.86
|
0.60
|
1.24
|
Standardized effect sizes (r) for contrasts: peaknmda
|
mutation
|
transfection
|
n
|
r
|
95% CI
|
|
R540H / WT
|
(+) / (-)
|
95
|
-0.02
|
[-0.22, 0.18]
|
|
R696H / WT
|
(+) / (-)
|
95
|
-0.04
|
[-0.24, 0.17]
|
|
C456Y / WT
|
(+) / (-)
|
95
|
-0.06
|
[-0.26, 0.14]
|
|
C461F / WT
|
(+) / (-)
|
95
|
-0.08
|
[-0.28, 0.12]
|

Analysis of decaynmda
All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: decaynmda
|
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT_vs_Mutants
|
1
|
0
|
0
|
0
|
|
LOF_vs_GOF
|
0
|
1
|
0
|
0
|
|
LOF
|
0
|
0
|
1
|
0
|
|
GOF
|
0
|
0
|
0
|
1
|
Sum of each orthogonal contrast should be zero: decaynmda
|
|
sum
|
|
WT_vs_Mutants
|
0
|
|
LOF_vs_GOF
|
0
|
|
LOF
|
0
|
|
GOF
|
0
|
Matrix of contrasts on mutation: decaynmda
|
mutation
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT
|
-0.8
|
0.0
|
0.0
|
0.0
|
|
R540H
|
0.2
|
0.5
|
0.0
|
-0.5
|
|
R696H
|
0.2
|
0.5
|
0.0
|
0.5
|
|
C456Y
|
0.2
|
-0.5
|
-0.5
|
0.0
|
|
C461F
|
0.2
|
-0.5
|
0.5
|
0.0
|
Matrix of contrasts on transfection: decaynmda
|
transfection
|
contrast
|
|
-
|
-0.5
|
|
+
|
0.5
|
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: decaynmda
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
mutation
|
2.67
|
4
|
15.78
|
.071
|
0.642
|
|
transfection
|
48.45
|
1
|
90.00
|
<.001
|
7.96e+06
|
|
mutation:transfection
|
8.85
|
4
|
90.00
|
<.001
|
1.52e+05
|
|
mutationWT_vs_Mutants:transfection1
|
19.20
|
1
|
90.00
|
<.001
|
|
|
mutationLOF_vs_GOF:transfection1
|
17.17
|
1
|
90.00
|
<.001
|
|
|
mutationLOF:transfection1
|
0.76
|
1
|
90.00
|
.386
|
|
|
mutationGOF:transfection1
|
0.24
|
1
|
90.00
|
.626
|
|
Intraclass correlation coefficients for random effects: decaynmda
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.000
|
95
|
|
slice:animal
|
0.000
|
62
|
|
animal
|
0.187
|
22
|
|
residual
|
0.813
|
190
|
Estimated marginal means with 95% confidence intervals: decaynmda
|
mutation
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
24.97
|
71.51
|
19
|
54.27
|
94.23
|
|
R540H
|
-
|
27.26
|
72.21
|
26
|
56.81
|
91.78
|
|
R696H
|
-
|
27.90
|
56.05
|
17
|
42.20
|
74.44
|
|
C456Y
|
-
|
32.47
|
71.19
|
18
|
54.71
|
92.62
|
|
C461F
|
-
|
23.62
|
99.03
|
15
|
72.36
|
135.54
|
|
WT
|
+
|
24.97
|
79.02
|
19
|
59.96
|
104.12
|
|
R540H
|
+
|
27.26
|
57.93
|
26
|
45.57
|
73.63
|
|
R696H
|
+
|
27.90
|
41.26
|
17
|
31.06
|
54.80
|
|
C456Y
|
+
|
32.47
|
34.47
|
18
|
26.49
|
44.85
|
|
C461F
|
+
|
23.62
|
40.38
|
15
|
29.50
|
55.27
|
[1] “
Overall average of decaynmda for untransfected neurons: 72.77”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: decaynmda
|
contrast
|
mutation
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
90
|
1.10
|
0.85
|
1.43
|
|
(+) / (-)
|
R540H
|
90
|
0.80
|
0.64
|
1.00
|
|
(+) / (-)
|
R696H
|
90
|
0.74
|
0.56
|
0.97
|
|
(+) / (-)
|
C456Y
|
90
|
0.48
|
0.37
|
0.63
|
|
(+) / (-)
|
C461F
|
90
|
0.41
|
0.31
|
0.54
|
95% confidence intervals for contrasts: decaynmda
|
mutation_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
R540H / WT
|
(+) / (-)
|
90
|
0.73
|
0.52
|
1.02
|
|
R696H / WT
|
(+) / (-)
|
90
|
0.67
|
0.46
|
0.97
|
|
C456Y / WT
|
(+) / (-)
|
90
|
0.44
|
0.30
|
0.63
|
|
C461F / WT
|
(+) / (-)
|
90
|
0.37
|
0.25
|
0.54
|
Standardized effect sizes (r) for contrasts: decaynmda
|
mutation
|
transfection
|
n
|
r
|
95% CI
|
|
R540H / WT
|
(+) / (-)
|
95
|
-0.19
|
[-0.38, 0.01]
|
|
R696H / WT
|
(+) / (-)
|
95
|
-0.22
|
[-0.41, -0.02]
|
|
C456Y / WT
|
(+) / (-)
|
95
|
-0.42
|
[-0.58, -0.24]
|
|
C461F / WT
|
(+) / (-)
|
95
|
-0.47
|
[-0.62, -0.30]
|

Analysis of dt50nmda
All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: dt50nmda
|
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT_vs_Mutants
|
1
|
0
|
0
|
0
|
|
LOF_vs_GOF
|
0
|
1
|
0
|
0
|
|
LOF
|
0
|
0
|
1
|
0
|
|
GOF
|
0
|
0
|
0
|
1
|
Sum of each orthogonal contrast should be zero: dt50nmda
|
|
sum
|
|
WT_vs_Mutants
|
0
|
|
LOF_vs_GOF
|
0
|
|
LOF
|
0
|
|
GOF
|
0
|
Matrix of contrasts on mutation: dt50nmda
|
mutation
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT
|
-0.8
|
0.0
|
0.0
|
0.0
|
|
R540H
|
0.2
|
0.5
|
0.0
|
-0.5
|
|
R696H
|
0.2
|
0.5
|
0.0
|
0.5
|
|
C456Y
|
0.2
|
-0.5
|
-0.5
|
0.0
|
|
C461F
|
0.2
|
-0.5
|
0.5
|
0.0
|
Matrix of contrasts on transfection: dt50nmda
|
transfection
|
contrast
|
|
-
|
-0.5
|
|
+
|
0.5
|
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: dt50nmda
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
mutation
|
4.62
|
4
|
16.09
|
.011
|
3.42
|
|
transfection
|
56.58
|
1
|
90.00
|
<.001
|
2.03e+07
|
|
mutation:transfection
|
10.45
|
4
|
90.00
|
<.001
|
1.19e+06
|
|
mutationWT_vs_Mutants:transfection1
|
21.18
|
1
|
90.00
|
<.001
|
|
|
mutationLOF_vs_GOF:transfection1
|
19.67
|
1
|
90.00
|
<.001
|
|
|
mutationLOF:transfection1
|
0.89
|
1
|
90.00
|
.349
|
|
|
mutationGOF:transfection1
|
1.72
|
1
|
90.00
|
.193
|
|
Intraclass correlation coefficients for random effects: dt50nmda
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.000
|
95
|
|
slice:animal
|
0.029
|
62
|
|
animal
|
0.255
|
22
|
|
residual
|
0.716
|
190
|
Estimated marginal means with 95% confidence intervals: dt50nmda
|
mutation
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
22.31
|
34.67
|
19
|
27.75
|
43.31
|
|
R540H
|
-
|
24.89
|
32.48
|
26
|
26.82
|
39.33
|
|
R696H
|
-
|
24.61
|
23.44
|
17
|
18.67
|
29.42
|
|
C456Y
|
-
|
28.03
|
35.05
|
18
|
28.42
|
43.22
|
|
C461F
|
-
|
21.32
|
35.98
|
15
|
27.90
|
46.38
|
|
WT
|
+
|
22.31
|
36.98
|
19
|
29.60
|
46.20
|
|
R540H
|
+
|
24.89
|
28.87
|
26
|
23.84
|
34.96
|
|
R696H
|
+
|
24.61
|
17.73
|
17
|
14.12
|
22.26
|
|
C456Y
|
+
|
28.03
|
20.35
|
18
|
16.50
|
25.10
|
|
C461F
|
+
|
21.32
|
18.35
|
15
|
14.23
|
23.65
|
[1] “
Overall average of dt50nmda for untransfected neurons: 31.95”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: dt50nmda
|
contrast
|
mutation
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
90
|
1.07
|
0.89
|
1.28
|
|
(+) / (-)
|
R540H
|
90
|
0.89
|
0.76
|
1.04
|
|
(+) / (-)
|
R696H
|
90
|
0.76
|
0.63
|
0.92
|
|
(+) / (-)
|
C456Y
|
90
|
0.58
|
0.48
|
0.70
|
|
(+) / (-)
|
C461F
|
90
|
0.51
|
0.42
|
0.62
|
95% confidence intervals for contrasts: dt50nmda
|
mutation_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
R540H / WT
|
(+) / (-)
|
90
|
0.83
|
0.66
|
1.06
|
|
R696H / WT
|
(+) / (-)
|
90
|
0.71
|
0.55
|
0.92
|
|
C456Y / WT
|
(+) / (-)
|
90
|
0.54
|
0.42
|
0.70
|
|
C461F / WT
|
(+) / (-)
|
90
|
0.48
|
0.36
|
0.63
|
Standardized effect sizes (r) for contrasts: dt50nmda
|
mutation
|
transfection
|
n
|
r
|
95% CI
|
|
R540H / WT
|
(+) / (-)
|
95
|
-0.16
|
[-0.35, 0.04]
|
|
R696H / WT
|
(+) / (-)
|
95
|
-0.26
|
[-0.44, -0.07]
|
|
C456Y / WT
|
(+) / (-)
|
95
|
-0.44
|
[-0.59, -0.26]
|
|
C461F / WT
|
(+) / (-)
|
95
|
-0.50
|
[-0.63, -0.33]
|

Analysis of chargenmda
All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: chargenmda
|
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT_vs_Mutants
|
1
|
0
|
0
|
0
|
|
LOF_vs_GOF
|
0
|
1
|
0
|
0
|
|
LOF
|
0
|
0
|
1
|
0
|
|
GOF
|
0
|
0
|
0
|
1
|
Sum of each orthogonal contrast should be zero: chargenmda
|
|
sum
|
|
WT_vs_Mutants
|
0
|
|
LOF_vs_GOF
|
0
|
|
LOF
|
0
|
|
GOF
|
0
|
Matrix of contrasts on mutation: chargenmda
|
mutation
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT
|
-0.8
|
0.0
|
0.0
|
0.0
|
|
R540H
|
0.2
|
0.5
|
0.0
|
-0.5
|
|
R696H
|
0.2
|
0.5
|
0.0
|
0.5
|
|
C456Y
|
0.2
|
-0.5
|
-0.5
|
0.0
|
|
C461F
|
0.2
|
-0.5
|
0.5
|
0.0
|
Matrix of contrasts on transfection: chargenmda
|
transfection
|
contrast
|
|
-
|
-0.5
|
|
+
|
0.5
|
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: chargenmda
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
mutation
|
3.00
|
4
|
16.28
|
.050
|
2.09
|
|
transfection
|
57.13
|
1
|
90.00
|
<.001
|
1.09e+08
|
|
mutation:transfection
|
3.63
|
4
|
90.00
|
.009
|
5.16
|
|
mutationWT_vs_Mutants:transfection1
|
4.81
|
1
|
90.00
|
.031
|
|
|
mutationLOF_vs_GOF:transfection1
|
10.02
|
1
|
90.00
|
.002
|
|
|
mutationLOF:transfection1
|
0.75
|
1
|
90.00
|
.389
|
|
|
mutationGOF:transfection1
|
0.13
|
1
|
90.00
|
.716
|
|
Intraclass correlation coefficients for random effects: chargenmda
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.094
|
95
|
|
slice:animal
|
0.219
|
62
|
|
animal
|
0.362
|
22
|
|
residual
|
0.325
|
190
|
Estimated marginal means with 95% confidence intervals: chargenmda
|
mutation
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
17.52
|
16.04
|
19
|
7.85
|
32.76
|
|
R540H
|
-
|
19.05
|
8.50
|
26
|
4.64
|
15.56
|
|
R696H
|
-
|
19.06
|
3.48
|
17
|
1.68
|
7.19
|
|
C456Y
|
-
|
21.16
|
11.34
|
18
|
5.83
|
22.06
|
|
C461F
|
-
|
17.19
|
8.66
|
15
|
3.81
|
19.67
|
|
WT
|
+
|
17.52
|
12.47
|
19
|
6.10
|
25.47
|
|
R540H
|
+
|
19.05
|
5.48
|
26
|
2.99
|
10.03
|
|
R696H
|
+
|
19.06
|
2.44
|
17
|
1.18
|
5.04
|
|
C456Y
|
+
|
21.16
|
4.91
|
18
|
2.52
|
9.54
|
|
C461F
|
+
|
17.19
|
2.99
|
15
|
1.32
|
6.79
|
[1] “
Overall average of chargenmda for untransfected neurons: 8.58”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: chargenmda
|
contrast
|
mutation
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
90
|
0.78
|
0.55
|
1.09
|
|
(+) / (-)
|
R540H
|
90
|
0.64
|
0.48
|
0.86
|
|
(+) / (-)
|
R696H
|
90
|
0.70
|
0.49
|
1.01
|
|
(+) / (-)
|
C456Y
|
90
|
0.43
|
0.30
|
0.61
|
|
(+) / (-)
|
C461F
|
90
|
0.35
|
0.24
|
0.51
|
95% confidence intervals for contrasts: chargenmda
|
mutation_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
R540H / WT
|
(+) / (-)
|
90
|
0.83
|
0.53
|
1.30
|
|
R696H / WT
|
(+) / (-)
|
90
|
0.90
|
0.55
|
1.48
|
|
C456Y / WT
|
(+) / (-)
|
90
|
0.56
|
0.34
|
0.91
|
|
C461F / WT
|
(+) / (-)
|
90
|
0.44
|
0.27
|
0.74
|
Standardized effect sizes (r) for contrasts: chargenmda
|
mutation
|
transfection
|
n
|
r
|
95% CI
|
|
R540H / WT
|
(+) / (-)
|
95
|
-0.09
|
[-0.28, 0.12]
|
|
R696H / WT
|
(+) / (-)
|
95
|
-0.04
|
[-0.24, 0.16]
|
|
C456Y / WT
|
(+) / (-)
|
95
|
-0.24
|
[-0.42, -0.04]
|
|
C461F / WT
|
(+) / (-)
|
95
|
-0.31
|
[-0.49, -0.12]
|

Analysis of peakampa
All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: peakampa
|
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT_vs_Mutants
|
1
|
0
|
0
|
0
|
|
LOF_vs_GOF
|
0
|
1
|
0
|
0
|
|
LOF
|
0
|
0
|
1
|
0
|
|
GOF
|
0
|
0
|
0
|
1
|
Sum of each orthogonal contrast should be zero: peakampa
|
|
sum
|
|
WT_vs_Mutants
|
0
|
|
LOF_vs_GOF
|
0
|
|
LOF
|
0
|
|
GOF
|
0
|
Matrix of contrasts on mutation: peakampa
|
mutation
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT
|
-0.8
|
0.0
|
0.0
|
0.0
|
|
R540H
|
0.2
|
0.5
|
0.0
|
-0.5
|
|
R696H
|
0.2
|
0.5
|
0.0
|
0.5
|
|
C456Y
|
0.2
|
-0.5
|
-0.5
|
0.0
|
|
C461F
|
0.2
|
-0.5
|
0.5
|
0.0
|
Matrix of contrasts on transfection: peakampa
|
transfection
|
contrast
|
|
-
|
-0.5
|
|
+
|
0.5
|
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: peakampa
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
mutation
|
9.08
|
4
|
16.25
|
<.001
|
59.3
|
|
transfection
|
0.77
|
1
|
90.00
|
.383
|
0.194
|
|
mutation:transfection
|
1.17
|
4
|
90.00
|
.328
|
0.197
|
|
mutationWT_vs_Mutants:transfection1
|
0.18
|
1
|
90.00
|
.677
|
|
|
mutationLOF_vs_GOF:transfection1
|
1.22
|
1
|
90.00
|
.273
|
|
|
mutationLOF:transfection1
|
3.41
|
1
|
90.00
|
.068
|
|
|
mutationGOF:transfection1
|
0.14
|
1
|
90.00
|
.706
|
|
Intraclass correlation coefficients for random effects: peakampa
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.087
|
95
|
|
slice:animal
|
0.180
|
62
|
|
animal
|
0.341
|
22
|
|
residual
|
0.392
|
190
|
Estimated marginal means with 95% confidence intervals: peakampa
|
mutation
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
18.09
|
5.72
|
19
|
3.17
|
10.31
|
|
R540H
|
-
|
19.79
|
18.10
|
26
|
10.97
|
29.86
|
|
R696H
|
-
|
19.77
|
51.28
|
17
|
28.14
|
93.44
|
|
C456Y
|
-
|
22.04
|
20.59
|
18
|
11.88
|
35.69
|
|
C461F
|
-
|
17.68
|
20.78
|
15
|
10.56
|
40.88
|
|
WT
|
+
|
18.09
|
5.05
|
19
|
2.80
|
9.12
|
|
R540H
|
+
|
19.79
|
18.10
|
26
|
10.97
|
29.87
|
|
R696H
|
+
|
19.77
|
55.67
|
17
|
30.55
|
101.43
|
|
C456Y
|
+
|
22.04
|
22.43
|
18
|
12.94
|
38.89
|
|
C461F
|
+
|
17.68
|
14.46
|
15
|
7.35
|
28.45
|
[1] “
Overall average of peakampa for untransfected neurons: 18.67”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peakampa
|
contrast
|
mutation
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
90
|
0.88
|
0.64
|
1.21
|
|
(+) / (-)
|
R540H
|
90
|
1.00
|
0.76
|
1.31
|
|
(+) / (-)
|
R696H
|
90
|
1.09
|
0.78
|
1.52
|
|
(+) / (-)
|
C456Y
|
90
|
1.09
|
0.79
|
1.51
|
|
(+) / (-)
|
C461F
|
90
|
0.70
|
0.49
|
0.99
|
95% confidence intervals for contrasts: peakampa
|
mutation_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
R540H / WT
|
(+) / (-)
|
90
|
1.13
|
0.75
|
1.72
|
|
R696H / WT
|
(+) / (-)
|
90
|
1.23
|
0.78
|
1.95
|
|
C456Y / WT
|
(+) / (-)
|
90
|
1.23
|
0.78
|
1.94
|
|
C461F / WT
|
(+) / (-)
|
90
|
0.79
|
0.49
|
1.27
|
Standardized effect sizes (r) for contrasts: peakampa
|
mutation
|
transfection
|
n
|
r
|
95% CI
|
|
R540H / WT
|
(+) / (-)
|
95
|
0.06
|
[-0.14, 0.26]
|
|
R696H / WT
|
(+) / (-)
|
95
|
0.09
|
[-0.11, 0.29]
|
|
C456Y / WT
|
(+) / (-)
|
95
|
0.10
|
[-0.11, 0.29]
|
|
C461F / WT
|
(+) / (-)
|
95
|
-0.10
|
[-0.30, 0.10]
|

Analysis of decayampa
All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: decayampa
|
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT_vs_Mutants
|
1
|
0
|
0
|
0
|
|
LOF_vs_GOF
|
0
|
1
|
0
|
0
|
|
LOF
|
0
|
0
|
1
|
0
|
|
GOF
|
0
|
0
|
0
|
1
|
Sum of each orthogonal contrast should be zero: decayampa
|
|
sum
|
|
WT_vs_Mutants
|
0
|
|
LOF_vs_GOF
|
0
|
|
LOF
|
0
|
|
GOF
|
0
|
Matrix of contrasts on mutation: decayampa
|
mutation
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT
|
-0.8
|
0.0
|
0.0
|
0.0
|
|
R540H
|
0.2
|
0.5
|
0.0
|
-0.5
|
|
R696H
|
0.2
|
0.5
|
0.0
|
0.5
|
|
C456Y
|
0.2
|
-0.5
|
-0.5
|
0.0
|
|
C461F
|
0.2
|
-0.5
|
0.5
|
0.0
|
Matrix of contrasts on transfection: decayampa
|
transfection
|
contrast
|
|
-
|
-0.5
|
|
+
|
0.5
|
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: decayampa
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
mutation
|
2.33
|
4
|
16.58
|
.099
|
1.37
|
|
transfection
|
1.42
|
1
|
90.00
|
.237
|
0.37
|
|
mutation:transfection
|
0.94
|
4
|
90.00
|
.446
|
0.16
|
|
mutationWT_vs_Mutants:transfection1
|
0.00
|
1
|
90.00
|
.956
|
|
|
mutationLOF_vs_GOF:transfection1
|
0.00
|
1
|
90.00
|
.990
|
|
|
mutationLOF:transfection1
|
3.73
|
1
|
90.00
|
.057
|
|
|
mutationGOF:transfection1
|
0.00
|
1
|
90.00
|
.994
|
|
Intraclass correlation coefficients for random effects: decayampa
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.000
|
95
|
|
slice:animal
|
0.210
|
62
|
|
animal
|
0.501
|
22
|
|
residual
|
0.289
|
190
|
Estimated marginal means with 95% confidence intervals: decayampa
|
mutation
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
17.51
|
8.33
|
19
|
5.05
|
13.73
|
|
R540H
|
-
|
18.88
|
6.97
|
26
|
4.59
|
10.58
|
|
R696H
|
-
|
18.52
|
3.44
|
17
|
2.08
|
5.70
|
|
C456Y
|
-
|
20.01
|
7.52
|
18
|
4.75
|
11.91
|
|
C461F
|
-
|
17.32
|
4.56
|
15
|
2.57
|
8.11
|
|
WT
|
+
|
17.51
|
7.92
|
19
|
4.80
|
13.05
|
|
R540H
|
+
|
18.88
|
6.58
|
26
|
4.33
|
9.99
|
|
R696H
|
+
|
18.52
|
3.24
|
17
|
1.96
|
5.38
|
|
C456Y
|
+
|
20.01
|
6.10
|
18
|
3.86
|
9.66
|
|
C461F
|
+
|
17.32
|
5.02
|
15
|
2.82
|
8.92
|
[1] “
Overall average of decayampa for untransfected neurons: 5.85”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: decayampa
|
contrast
|
mutation
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
90
|
0.95
|
0.77
|
1.17
|
|
(+) / (-)
|
R540H
|
90
|
0.94
|
0.79
|
1.13
|
|
(+) / (-)
|
R696H
|
90
|
0.94
|
0.76
|
1.17
|
|
(+) / (-)
|
C456Y
|
90
|
0.81
|
0.66
|
1.00
|
|
(+) / (-)
|
C461F
|
90
|
1.10
|
0.87
|
1.39
|
95% confidence intervals for contrasts: decayampa
|
mutation_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
R540H / WT
|
(+) / (-)
|
90
|
0.99
|
0.76
|
1.30
|
|
R696H / WT
|
(+) / (-)
|
90
|
0.99
|
0.74
|
1.34
|
|
C456Y / WT
|
(+) / (-)
|
90
|
0.85
|
0.64
|
1.15
|
|
C461F / WT
|
(+) / (-)
|
90
|
1.16
|
0.85
|
1.58
|
Standardized effect sizes (r) for contrasts: decayampa
|
mutation
|
transfection
|
n
|
r
|
95% CI
|
|
R540H / WT
|
(+) / (-)
|
95
|
-0.01
|
[-0.21, 0.20]
|
|
R696H / WT
|
(+) / (-)
|
95
|
-0.01
|
[-0.21, 0.20]
|
|
C456Y / WT
|
(+) / (-)
|
95
|
-0.11
|
[-0.31, 0.09]
|
|
C461F / WT
|
(+) / (-)
|
95
|
0.10
|
[-0.11, 0.29]
|

Analysis of dt50ampa
All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: dt50ampa
|
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT_vs_Mutants
|
1
|
0
|
0
|
0
|
|
LOF_vs_GOF
|
0
|
1
|
0
|
0
|
|
LOF
|
0
|
0
|
1
|
0
|
|
GOF
|
0
|
0
|
0
|
1
|
Sum of each orthogonal contrast should be zero: dt50ampa
|
|
sum
|
|
WT_vs_Mutants
|
0
|
|
LOF_vs_GOF
|
0
|
|
LOF
|
0
|
|
GOF
|
0
|
Matrix of contrasts on mutation: dt50ampa
|
mutation
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT
|
-0.8
|
0.0
|
0.0
|
0.0
|
|
R540H
|
0.2
|
0.5
|
0.0
|
-0.5
|
|
R696H
|
0.2
|
0.5
|
0.0
|
0.5
|
|
C456Y
|
0.2
|
-0.5
|
-0.5
|
0.0
|
|
C461F
|
0.2
|
-0.5
|
0.5
|
0.0
|
Matrix of contrasts on transfection: dt50ampa
|
transfection
|
contrast
|
|
-
|
-0.5
|
|
+
|
0.5
|
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: dt50ampa
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
mutation
|
3.00
|
4
|
16.11
|
.050
|
1.58
|
|
transfection
|
2.96
|
1
|
90.00
|
.089
|
0.694
|
|
mutation:transfection
|
0.31
|
4
|
90.00
|
.871
|
0.0663
|
|
mutationWT_vs_Mutants:transfection1
|
0.87
|
1
|
90.00
|
.354
|
|
|
mutationLOF_vs_GOF:transfection1
|
0.05
|
1
|
90.00
|
.830
|
|
|
mutationLOF:transfection1
|
0.04
|
1
|
90.00
|
.839
|
|
|
mutationGOF:transfection1
|
0.31
|
1
|
90.00
|
.579
|
|
Intraclass correlation coefficients for random effects: dt50ampa
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.100
|
95
|
|
slice:animal
|
0.012
|
62
|
|
animal
|
0.286
|
22
|
|
residual
|
0.602
|
190
|
Estimated marginal means with 95% confidence intervals: dt50ampa
|
mutation
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
20.62
|
4.28
|
19
|
3.21
|
5.71
|
|
R540H
|
-
|
23.15
|
3.87
|
26
|
3.01
|
4.96
|
|
R696H
|
-
|
22.52
|
2.60
|
17
|
1.93
|
3.49
|
|
C456Y
|
-
|
25.36
|
3.96
|
18
|
3.02
|
5.20
|
|
C461F
|
-
|
19.69
|
3.11
|
15
|
2.24
|
4.33
|
|
WT
|
+
|
20.62
|
4.30
|
19
|
3.22
|
5.75
|
|
R540H
|
+
|
23.15
|
3.67
|
26
|
2.86
|
4.70
|
|
R696H
|
+
|
22.52
|
2.28
|
17
|
1.70
|
3.06
|
|
C456Y
|
+
|
25.36
|
3.48
|
18
|
2.65
|
4.56
|
|
C461F
|
+
|
19.69
|
2.82
|
15
|
2.03
|
3.92
|
[1] “
Overall average of dt50ampa for untransfected neurons: 3.51”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: dt50ampa
|
contrast
|
mutation
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
90
|
1.01
|
0.82
|
1.24
|
|
(+) / (-)
|
R540H
|
90
|
0.95
|
0.79
|
1.13
|
|
(+) / (-)
|
R696H
|
90
|
0.88
|
0.70
|
1.09
|
|
(+) / (-)
|
C456Y
|
90
|
0.88
|
0.71
|
1.09
|
|
(+) / (-)
|
C461F
|
90
|
0.91
|
0.72
|
1.14
|
95% confidence intervals for contrasts: dt50ampa
|
mutation_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
R540H / WT
|
(+) / (-)
|
90
|
0.94
|
0.72
|
1.24
|
|
R696H / WT
|
(+) / (-)
|
90
|
0.87
|
0.64
|
1.18
|
|
C456Y / WT
|
(+) / (-)
|
90
|
0.87
|
0.65
|
1.17
|
|
C461F / WT
|
(+) / (-)
|
90
|
0.90
|
0.66
|
1.23
|
Standardized effect sizes (r) for contrasts: dt50ampa
|
mutation
|
transfection
|
n
|
r
|
95% CI
|
|
R540H / WT
|
(+) / (-)
|
95
|
-0.04
|
[-0.24, 0.16]
|
|
R696H / WT
|
(+) / (-)
|
95
|
-0.09
|
[-0.29, 0.11]
|
|
C456Y / WT
|
(+) / (-)
|
95
|
-0.10
|
[-0.29, 0.11]
|
|
C461F / WT
|
(+) / (-)
|
95
|
-0.07
|
[-0.27, 0.13]
|

Analysis of chargeampa
All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: chargeampa
|
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT_vs_Mutants
|
1
|
0
|
0
|
0
|
|
LOF_vs_GOF
|
0
|
1
|
0
|
0
|
|
LOF
|
0
|
0
|
1
|
0
|
|
GOF
|
0
|
0
|
0
|
1
|
Sum of each orthogonal contrast should be zero: chargeampa
|
|
sum
|
|
WT_vs_Mutants
|
0
|
|
LOF_vs_GOF
|
0
|
|
LOF
|
0
|
|
GOF
|
0
|
Matrix of contrasts on mutation: chargeampa
|
mutation
|
WT_vs_Mutants
|
LOF_vs_GOF
|
LOF
|
GOF
|
|
WT
|
-0.8
|
0.0
|
0.0
|
0.0
|
|
R540H
|
0.2
|
0.5
|
0.0
|
-0.5
|
|
R696H
|
0.2
|
0.5
|
0.0
|
0.5
|
|
C456Y
|
0.2
|
-0.5
|
-0.5
|
0.0
|
|
C461F
|
0.2
|
-0.5
|
0.5
|
0.0
|
Matrix of contrasts on transfection: chargeampa
|
transfection
|
contrast
|
|
-
|
-0.5
|
|
+
|
0.5
|
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: chargeampa
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
mutation
|
5.90
|
4
|
15.68
|
.004
|
10.7
|
|
transfection
|
1.68
|
1
|
90.00
|
.198
|
0.245
|
|
mutation:transfection
|
1.84
|
4
|
90.00
|
.128
|
0.482
|
|
mutationWT_vs_Mutants:transfection1
|
1.88
|
1
|
90.00
|
.174
|
|
|
mutationLOF_vs_GOF:transfection1
|
2.70
|
1
|
90.00
|
.104
|
|
|
mutationLOF:transfection1
|
3.38
|
1
|
90.00
|
.069
|
|
|
mutationGOF:transfection1
|
0.05
|
1
|
90.00
|
.829
|
|
Intraclass correlation coefficients for random effects: chargeampa
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.177
|
95
|
|
slice:animal
|
0.139
|
62
|
|
animal
|
0.195
|
22
|
|
residual
|
0.489
|
190
|
Estimated marginal means with 95% confidence intervals: chargeampa
|
mutation
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
19.19
|
0.29
|
19
|
0.18
|
0.48
|
|
R540H
|
-
|
20.31
|
0.83
|
26
|
0.54
|
1.28
|
|
R696H
|
-
|
21.86
|
1.27
|
17
|
0.76
|
2.11
|
|
C456Y
|
-
|
25.47
|
1.03
|
18
|
0.64
|
1.65
|
|
C461F
|
-
|
18.59
|
0.79
|
15
|
0.45
|
1.39
|
|
WT
|
+
|
19.19
|
0.33
|
19
|
0.20
|
0.54
|
|
R540H
|
+
|
20.31
|
0.85
|
26
|
0.55
|
1.30
|
|
R696H
|
+
|
21.86
|
1.22
|
17
|
0.73
|
2.04
|
|
C456Y
|
+
|
25.47
|
0.97
|
18
|
0.60
|
1.55
|
|
C461F
|
+
|
18.59
|
0.46
|
15
|
0.26
|
0.81
|
[1] “
Overall average of chargeampa for untransfected neurons: 0.76”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: chargeampa
|
contrast
|
mutation
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
90
|
1.12
|
0.79
|
1.57
|
|
(+) / (-)
|
R540H
|
90
|
1.02
|
0.76
|
1.36
|
|
(+) / (-)
|
R696H
|
90
|
0.97
|
0.67
|
1.38
|
|
(+) / (-)
|
C456Y
|
90
|
0.94
|
0.67
|
1.34
|
|
(+) / (-)
|
C461F
|
90
|
0.58
|
0.40
|
0.86
|
95% confidence intervals for contrasts: chargeampa
|
mutation_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
R540H / WT
|
(+) / (-)
|
90
|
0.91
|
0.58
|
1.42
|
|
R696H / WT
|
(+) / (-)
|
90
|
0.86
|
0.53
|
1.42
|
|
C456Y / WT
|
(+) / (-)
|
90
|
0.85
|
0.52
|
1.38
|
|
C461F / WT
|
(+) / (-)
|
90
|
0.52
|
0.31
|
0.87
|
Standardized effect sizes (r) for contrasts: chargeampa
|
mutation
|
transfection
|
n
|
r
|
95% CI
|
|
R540H / WT
|
(+) / (-)
|
95
|
-0.04
|
[-0.24, 0.16]
|
|
R696H / WT
|
(+) / (-)
|
95
|
-0.06
|
[-0.26, 0.14]
|
|
C456Y / WT
|
(+) / (-)
|
95
|
-0.07
|
[-0.27, 0.13]
|
|
C461F / WT
|
(+) / (-)
|
95
|
-0.26
|
[-0.43, -0.06]
|
